Abstract Two general types of genetic alterations drive cancer progression; mutations and copy number alterations (CNAs). Research into mutations such ABL fusions and BRAF have yielded powerful targeted therapeutics. However, not all cancers are mutated in targetable genes; 48% of serous ovarian cancers (OV) have no oncogenic mutation other than in p53. For these low-mutation tumors and cancer types, the most likely culprit for tumorigenesis and drug resistance lies in CNAs. The OV genome is remarkably unstable; in the average tumor, 2/3 of genes display a copy number change: roughly 1/3 are deleted and 1/3 are increased in gene dosage. One known CNA driver in ovarian cancer is a homozygous loss in BRCA1/2 genes in ~10% of patients; however 99% of deletions in SOC are heterozygous, not homozygous deletions. This remaining 99% of deletions must contain tumor suppressors which contribute to cancer progression with only heterozygous losses, which accumulate along individual pathways. We developed novel HAPTRIG pathway analysis of loss events in whole genome datasets with the ability to work in highly altered backgrounds like OV and can perform calculations of multiple pathways at once. We discovered that the cellular recycling pathway of autophagy is universally (98% of tumors), redundantly (at least 4 genes are deleted in the average tumor), and uniquely (more than any other tumor type) suppressed by deletions in serous ovarian cancer. The most impactful lost autophagy genes are BECN1 and LC3B. We found BECN1 and LC3B loss is to contribute to OV aneuploidy and monoallelic BECN1 loss to accelerate OV tumorigenesis in a mouse model. We propose to develop our understanding of tumor CNAs by [1] analyzing every tumor for pathway disruptions in >3,000 known molecular pathways using an automated HAPTRIG bioinformatics tool, [2] scoring the most impactful genes in each pathway/tumor pair to identify novel CNA drivers of cancer, and [3] release the tool in a user- friendly portal for any oncologist to perform CNA pathway analysis on any cohort of tumors. Since our top predictions from the CNA networks were validated to impact genomic copy number variability, oncogenesis, and therapy targeting in OV, we propose to provide further mechanistic understanding of CNA losses by [1] analyzing the types and heterogeneity of CNAs caused by BECN1 and LC3B depletion, [2] the metabolic, CNA, and stem cell changes present in BECN1+/- murine OV tumors, and [3] assaying autophagic flux and metabolic alterations for chloroquine therapy, which selectively kills BECN1 and LC3B depleted OV cells.